Artificial computer neural networks are able to learn from themselves by repeatedly running a set of instructions or algorithm, and learning each time from the previous results. Computer networks which are used by financial institutions can have and maintain these elaborate artificial neural network schemes in order to track billions of transactions every hour. These learning systems may improve upon themselves in order to protect identities as well as preserve truthful transactions.
Enterprise systems frequently utilize rules and rules engines to determine which transactions to allow. For example, a consumer using a payment card may attempt to make a legitimate transaction in person or online. An attempt may similarly be made in a fraudulent manner with a user using an illegitimate or stolen payment card. Enterprise and banking systems may devise their rules such as to prevent these transactions. In order to prevent these transactions, however, a system must be implemented to detect who and when one may use an authorized payment method. Laurene Fausett's Fundamentals of Neural Networks Architectures, Algorithms and Applications, published by Pearson Education in 2004, and incorporated by reference, includes examples of systems known in the art.
In some systems which are overly cautious, one out of ten detected transactions may be fraud while the other nine are valid transactions. This system may frustrate users with repeated authorization requests or denied transactions. There is a need, therefore, for better identification of false positives, as well as improved rules systems' mechanisms.